When was the last time you found all addresses in your list follow the same format and were error-free? Never, right?
Despite all the steps your company may take to minimize data errors, address data quality issues – such as misspellings, missing fields, or leading spaces – due to manual data entry – are inevitable.
In fact, Professor Raymond R. Panko in his published paper highlighted that spreadsheet data errors especially of small datasets can range between 18% and 40%.
To combat this problem, address standardization can be a great solution. This post highlights how companies can benefit from standardizing data, and what methods and tips they should consider to bring about intended results.
What is Address Standardization?
Address standardization, or address normalization, is the process of identifying and formatting address records in line with recognized postal service standards as laid out in an authoritative database such as that of the United States Postal Service (USPS).
Most addresses do not follow the USPS standard, which defines a standardized address as, ‘one that is fully spelled out, abbreviated using the Postal Service standard abbreviations, or as shown in the current Postal Service ZIP+4 file.’
Standardizing addresses becomes a pressing need for companies that have address entries with inconsistent or varying formats due to missing address details (e.g., ZIP+4 and ZIP+6 codes) or punctuation, casing, spacing, and spelling errors. An example of this is given below:
As seen from the table, all address details have one or multiple errors and none meet the required USPS guidelines.
Address standardization should not be confused with address matching and address validation. While there are similar, address validation is about verifying if an address record conforms to an existing address record in the USPS database. Address matching, on other hand, is about matching two similar address data to ascertain if it refers to the same entity or not.
Benefits of Standardizing Addresses
Apart from the obvious reasons of cleansing data anomalies, standardizing addresses can provide an array of benefits for companies. These include:
- Save time verifying addresses: without standardizing addresses, there is no way to suspect if the address list used for the direct mail campaign is accurate or not unless the mails are returned or have got no responses. By normalizing varying addresses, substantial man-hours can be saved by staff sifting through hundreds of mailing addresses for accuracy.
- Reduce mailing costs: Direct mail campaigns can lead to wrong or incorrect addresses that can create billing and shipping issues in direct mail campaigns. Standardizing addresses to improve data consistency can reduce returned or undelivered mails, resulting in higher direct mail response rates.
- Eliminate duplicate addresses: varying formats and addresses with errors can result in sending twice as many emails to contacts that can lower customer satisfaction and brand image. Cleaning your address lists can help your firm save wasted delivery costs.
How to Standardize Addresses?
Any address normalization activity should meet USPS guidelines for it to be worthwhile. Using the data highlighted in Table 1, here is how address data will appear upon normalization.
Standardizing addresses involves a 4-step process. This includes:
- Import addresses: gather all addresses from multiple data sources – such as Excel spreadsheets, SQL databases, etc. – into one sheet.
- Profile data to inspect errors: carry out data profiling using to understand the scope and type of errors present in your address list. Doing this can give you a rough idea of the potential problem areas that require fixing before carrying out any kind of standardization.
- Clean errors to meet USPS guidelines: Once all errors are detected, you can then cleanse the addresses and standardize it in accordance with USPS guidelines.
- Identify and remove duplicate addresses: to identify any duplicate addresses, you can search for double counts in your spreadsheet or database or use exact or fuzzy matching to dedupe entries.
Methods of Standardizing Addresses
There are two distinct approaches to normalizing addresses in your list. These include:
Manual Scripts and Tools
Users can manually find run scripts and add-ins to normalize addresses from libraries via various
- Programming languages: Python, JavaScript, or R can enable you to run fuzzy address matching to identify inexact address matches and apply custom standardization rules to suit your own address data.
- Coding repositories: GitHub provides code templates and USPS API integration that you can use to verify and normalize addresses.
- Excel-based tools: add-ins and solutions such as YAddress, AddressDoctor Excel Plugin, or excel VBA Master can help you parse and standardize your addresses within your datasets.
A few benefits of going down this route is that it is inexpensive and can be quick to normalize data for small datasets. However, using such scripts can fall apart beyond a few thousand records and thus are not suited for very large datasets or those spread across disparate sources.
Address Verification Software
An off-the-shelf address verification and normalization software can also be used to normalize data. Usually, such tools come with specific address validation components – such as an integrated USPS database – and have out-of-the-box data profiling and cleansing components along with fuzzy matching algorithms to standardize addresses at scale.
It is also important that the software has CASS certification from USPS and meets the required accuracy threshold in terms of:
- 5-digit coding
- ZIP+4 coding
- RDI
- DPV
- eLOT
- LACSLink
- SuiteLINK and much more
The main advantages are the ease at which it can verify and standardize address data stored in disparate systems including CRMs, RDBMs and Hadoop-based repositories and geocode data to yield longitude and latitude values.
As for limitations, such tools can cost far more than manual address normalization methods.
Which Method Is Better?
Choosing the right method for enhancing your address lists depends entirely on the volume of your address records, technology stack, and project timeline.
If your address list is less than say five thousand records, standardizing it through Python or JavaScript can be a better option. However, if achieving a single source of truth for addresses using data spread in multiple sources within a timely manner is a pressing need then a CASS-certified address standardization software can be a better option.
